Monthly Archives: September 2016

After Benford’s law, which describes frequency distribution of leading digits in datasets and is often used for accounting fraud detection, data scientists developed a new method which helps to uncover fraud using data analysis. In a paper from 2016, Dmitry Kobak, Sergey Shpilkin and Maxim S. Pshenichnikov show that the results of Russian elections have some very disturbing artefacts indicating fraudulent behavior at polling stations.

The authors started with a simple assumption that people, when making up numbers, tend to go with round integers. So if polling stations do not report the real results but made-up numbers instead, there would be a disproportionate count of polling stations reporting round and neat percentages. This natural inclination to round numbers can be only intensified by thresholds that the central authority considers as “success”.

To test that some polling stations really do make up the reported numbers, the authors used a Monte Carlo simulation to estimate likelihoods of the whole spectrum of percentage results. This way, they were able to find 99.99% confidence intervals for the number of polling stations reporting round results. Their careful analysis shows that there are indeed improbable spikes in the empirical distributions which can be hardly explained in any other way than by fraud.

Interestingly, this phenomenon can be observed since the presidential elections in 2004 when Vladimir Putin was seeking his first reelection. The analysis works with the data from Russian elections in years 2000 till 2012, and it is only the early elections of 2000 and 2003 that do not suggest manipulation in the vote count. The paper does not attempt to answer the question of what the driver of this turning point is, but it is symptomatic that the regions showing persistent anomalies are largely located in the North Caucasian Federal District (e.g. Chechnya).

As a control, the same method is employed using data from German, Polish and Spanish elections. None of these countries show suspicious spikes in the data. Could a group of statisticians serve as a watchdog for national elections? It certainly seems so! Although fraudulent governments can randomize in their vote count manipulation or simply use other dishonest methods to influence democratic elections, let us hope that the honest data scientist will be always step ahead uncovering dirty practices.

It is commonly accepted that a minimum wage increase has two direct effects on health. These effects result from the Grossman model, which is heavily used in economics of health. On the one hand, minimum wage increases allow individuals with low income to purchase more market goods that improve their health, for example better medical care and better food. On the other hand, it increases the opportunity cost of not working and thus makes non-market goods consumption (sport, relax) more expensive. Not surprisingly, the overall effect seems heterogeneous and differs for cohorts.

To shed more light on minimum wage effects on health, B. Horn, J. Catherine Maclean, and M. Strain analyzed data about lesser-skilled workers. As they concluded, the results fail to suggest any indisputable general improvements of the people’s health. The effect depends upon the particular group of people. While workers tend to report better health conditions after minimum wage increases, the unemployed are more likely to be negatively affected. Overall, the contribution of this study lies in providing a more comprehensive view on minimum wage policy and its consequences. Notably, the authors focused on more than just the potential decline in employment of marginal workers and recognized also additional social and medical issues.

The importance of online customer-to-customer (C2C) marketplaces has been growing and nowadays Taobao, the biggest platform in China, has 500 millions of registered users. Such platforms have a common inherent issue – the presence of asymmetric information and adverse selection problems, which obstruct trade. Fortunately, the online world allows buyers to leave feedback assessing how much they were satisfied with the bought items. However, it is not that easy. It turns out that feedback has one of the most characteristic aspects of public goods – everybody would appreciate it, but only a minority of consumers are willing to provide it.

Nevertheless, as Lingfang Li, Steven Tadelis, and Xiaolan Zhoushowed showed, if consumers are motivated to leave a feedback, they do so. As a result, information asymmetry is reduced. In particular, the authors studied roughly 7 million transactions made on Taobao between September 2012 and February 2013. As a measure to sweeten online shopping, Taobao introduced a “rebate-for-feedback” reward system. This mechanism allows sellers to offer part of the paid amount to be returned back to buyer if he or she met certain conditions and left a fine feedback (not necessarily a positive one). The authors’ main results suggest that high quality sellers are more likely to ask for a review and also the reviews tend to be of higher quality (measured as the length of the rating).

It has been a tricky question for such a long period of time: are marijuana and tobacco substitutes or complements? Leaving the economic terminology aside: do people tend to use tobacco and marijuana at the same time or do they rather alternate between the two? Since several states in the US have recently agreed to legalize (medical) marijuana use, economists were finally given the opportunity to collect and study US data about consumption habits of both.

Having gathered the data, Anna Choi, Dhaval Dave, and Joseph J. Sabia ran a difference-in-differences regression with the aim to estimate whether and to what extent the legalization of marijuana affects the consumption of cigarettes. The empirical results suggest that after the legalization of marijuana the consumption of cigarettes tends to decrease. In other words, tobacco and marijuana seem to be substitutes. If the results prove to be right, it may have important implications for policy-makers. For example, an increase in tax burden levied on tobacco might increase the demand for marijuana. Or, on the contrary, legal usage of marijuana might decrease the sales of tobacco products and thus decrease the amount of taxes collected from tobacco.

In one episode of The Big Bang Theory, Sheldon Cooper let dice to make decisions for him. A similar experiment was conducted by Steven Levitt. However, instead of studying decisions that affected him and his life, he investigated the decision process of people in a field experiment. In particular, he collected data about more than 20 000 people who faced an important decision (quitting a job, leaving a spouse or going back to school) and let them toss a coin. As a rule, when a head appeared the subject was asked to make the change, whereas a tail stands for maintaining the status quo for at least two months. After 2 and then also 6 months the participants were given a survey asking, among others, about their happiness.

As the author claims himself, there are two main research questions: (i) do the participants obey the coin and do as it says; and (ii) is there any impact on their reported level of happiness? Assuming that only the marginal agents join this experiment and thus a half of them are expected to take an action if there were no coin toss and the coin is fair, means that if there is no real effect of the coin then actions of 50% the participant should coincide with the coin suggestions. However, in this case, as the author reports, significantly more than 50% of participants follow the coin recommendation. In particular, while in more important issues it is around 55%, the ratio is even higher for the less important issues – 67%.

When it comes to the causal effect on happiness, using slightly more advanced statistical methods to combat the endogeneity problem, Steven Levitt argues that those individuals whose coin came up heads (take a change) report being happier than those with tails (status quo). It can be because having a head motivates to take a change which he/she would rather postpone or do not take at all (status quo biased observed in normal life). Overall, even though the conclusion may be biased for several reasons, it plants a seed of doubt if we would not be better off letting (at least the less important) decision were taken randomly.

Reference: Levitt, S. D. (2016). Heads or Tails: The Impact of a Coin Toss on Major Life Decisions and Subsequent Happiness (No. w22487). National Bureau of Economic Research. Available here.

How difficult is it to find a fund manager to whom you would entrust your money; to assess his quality before it is too late? Apparently, one way is to look at his family background as it has been recently shown to be a decent signal of future performance. In particular, Chuprinin and Sosyura (2016) found that fund managers from poor families outperform those coming from a wealthier background.

To explain this phenomena, the authors argued that wealthy family background makes it easier to move up into a managerial position as the applicants face less barriers. In contrast, for an applicant from a poor family it is heavy going. As a result, to succeed as a manager with a poor family background, one needs to possess top skills. In other words, the selection process causes that while the rich applicants do not need to meet the highest criteria, the poor ones do.

The authors also claimed that it would necessarily mean that there is more dispersion among the fund managers from rich families as not all of them satisfy the highest criteria. On the contrary, performance of the successful applicants from less rich families are likely to be more similar. As a matter of fact, they found higher volatility in the results among the fund managers with wealthier background and thus provided evidence in favor of their arguments. The picture shows the distribution of income of the general male population and that of the managers’ fathers.

Reference: Chuprinin, O., & Sosyura, D. (2016, May). Family Descent as a Signal of Managerial Quality: Evidence from Mutual Funds. In University of Miami, School of Business Administration, 6th Miami Behavioral Finance Conference. Available here.

The presence of universities in a particular region is argued to have a positive effect on GDP per capita. To test this claim empirically, Valero and Reenen (2016) made use of a huge database of almost 15000 universities in 1500 regions. Their empirical exercise suggests that there is a positive and significant impact of the existence of a university in a region on the region’s GDP per capita and this conclusion seems to be robust to different specifications. They found that a doubled number of universities is associated with a 4% increase of GDP per capita.

Additionally, the authors identified four main channels through which the positive effect is likely to influence the economy. In particular, unsurprisingly, more universities increase human capital which is believed to increase productivity and thus the GDP per capita as well. Apart from human capital, universities and their environment strengthen innovation activity, measured by the number of registered patents. Further, it is widely known that institutions represent a key determinant of economic growth. Specifically, some of the institutions such as democracy and political culture are claimed to be necessary for growth, especially in developed countries. Finally, there is a direct effect of higher economic activity from the existence of the university in a particular region (construction of the buildings etc.) and higher demand from professors and students. The empirical results indicate that growth is driven by both human capital and innovation, though the effects of these are small in magnitude. The impact of democracy and institutions also seems to be positive although rather in the long term. To sum up, the authors conclude that the presence of universities impacts growth also in other ways than simply via the increase in demand caused by higher economic activity.